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热带气旋气候持续性降水模型R-CLIPER本地化研究

Study on the localization of tropical cyclone rainfall climatology and persistence model R-CLIPER

  • 摘要: 热带气旋(简称TC)气候持续性降水模型(简称R-CLIPER)是基于对北大西洋TC或全球TC观测的一种TC降水统计参数模型,具有输入简单、计算快捷的优点,可实现气候尺度TC降水模拟,并为TC降水预报和风险预估提供技术支撑。以西北太平洋地区TC为研究对象,对R-CLIPER模型进行本地化研究。首先,基于热带测雨卫星(TRMM)及风云卫星(FY2C/2E)降水数据,提取TC不同强度等级(即超强台风、强台风、台风、强热带风暴、热带风暴、热带低压共6个)的降水径向平均廓线;其次,结合R-CLIPER模型框架,研究面向全局最优的TC降水廓线参数化拟合方法,分别构建基于TRMM和FY2C/2E降水数据的西北太平洋R-CLIPER,即TRMM-R-CLIPER-WNP(模型1)和FY-R-CLIPER-WNP(模型2);最后,针对2012—2013年西北太平洋62个TC及2009—2021年影响浙江的26个TC,从拟合误差、降水落区两个方面对模型进行评估。结果表明:(1)TC的TRMM降水廓线在峰值区相对尖锐,而FY2C/2E廓线则更平滑、细节刻画更精细,可能原因是后者分辨率更高;(2) TRMM廓线和FY廓线参数化的均方根误差(RMSE)分别为0.28mm·h-1、0.51mm·h-1;(3) 基于站点数据TC降水落区评分表明,当降水阈值小于3mm·h-1时,模型1模拟值的ETS评分优于模型2,当阈值大于3mm·h-1时模型2结果更优,两者均表现出TC中心海上比陆上评分更优,即两者均表现出热带气旋的中心降水强度预报海上准确率比陆上更高。(4)使用参数化拟合方法,可将R-CLIPER模型在西北太平洋地区TC降水模拟的准确度提升5.5%;同时还揭示出TC降水模拟误差受模型数据源、模型框架、参数化方案等因子影响。

     

    Abstract: The Tropical Cyclone (TC) Rainfall Climatology and Persistence Model (R-CLIPER) is a statistical parametric model for TC precipitation based on observations of North Atlantic TCs or global TCs. It boasts simple inputs and quick calculations, enabling climate-scale TC precipitation simulations, thus providing technical support for TC precipitation forecasting and risk assessments. This study focuses on TCs in the Western North Pacific region and performs localization of the R-CLIPER model. Firstly, based on precipitation data from Tropical Rainfall Measuring Mission (TRMM) satellite and Fengyun satellite (FY2C/2E) precipitation data, the radial average profiles of precipitation from TCs of 6 different intensity levels (i.e., super typhoon, strong typhoon, typhoon, severe tropical storm, tropical storm, tropical depression) are extracted. Secondly, combined with the R-CLIPER model framework, a globally optimal parameterization fitting method for TC precipitation profiles is developed. Two models are constructed, including the R-CLIPER for the Western North Pacific Ocean based on TRMM data/FY data, namely TRMM-R-CLIPER-WNP (Model 1) and FY-R-CLIPER-WNP (Model 2). Finally, based on the 62 tropical cyclones in the Northwest Pacific from 2012 to 2013 and the 26 cyclones that affected Zhejiang from 2009 to 2021 the models are evaluated in terms of fitting error and precipitation falling area. The results are as follows. (1) The TC rainfall profiles derived from TRMM data are relatively sharp near the maximum rainfall, while those from FY2C/2E satellite data are smoother and more detailed due to its higher spatial resolution. (2) The RMSE of TRMM parameterized profiles is 0.28 mm·h-1, and that of FY2C/2E profiles is 0.51 mm·h-1. (3) Based on the ground-based station data evaluation, when the rainfall threshold is less than 3 mm·h-1, the ETS score of Model 1 is better than that of Model 2. But when the threshold is greater than 3 mm·h-1, Model 2 is better. Both models perform better when the TC is centered over the sea than over land, indicating higher accuracy of TC central rainfall intensity prediction over the ocean than over land. In conclusion, using the parameterized fitting method, the accuracy of the R-CLIPER model in simulating TC precipitation in the Northwest Pacific region can be improved by 5.5%. Additionally, it was revealed that the error in TC precipitation simulations is influenced by factors such as model data sources, model framework, and parameterization schemes.

     

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